Temporal coherent adaptive target detection for multi-input multi-output radars in clutter

The superiority of multiple-input multiple-output (MIMO) radars over conventional phased-array radars has been recently shown in many aspects. The MIMO radars have better detection performance and can extract target information more precisely than phased-array radars. The problem of adaptive target detection using temporal coherent pulse train in the presence of nonhomogeneous clutter (different clutter statistics in receivers) is considered for MIMO radars. This problem has been formulated as a hypothesis test. For clutter with unknown statistics three adaptive decision rules have been developed using the generalised likelihood ratio test. The results show the superiority of the MIMO radars with temporal coherent processing over conventional phased arrays in the presence of clutter.     

Performance of the adaptive generalised matched subspace constant false alarm rate detector in non-Gaussian noise: an experimental analysis

The authors deal with the performance analysis of an adaptive version of the generalised matched subspace detector (GMSD) in compound-Gaussian clutter with unknown covariance matrix. The original GMSD was proposed to detect subspace signals in compound-Gaussian noise with known covariance matrix and ensures the constant false alarm rate (CFAR) property. In real situations, this assumption is unrealistic, which means that the covariance matrix must be estimated from training data. The authors use a robust estimate of the covariance matrix called the fixed-point estimate, recently proposed in the literature. The performance of the obtained adaptive detector, in terms of CFAR behaviour and probability of detection, is evaluated in the presence of real sea clutter data, collected by the McMaster IPIX radar.     

Radar detection based on Bayesian estimation of target amplitude

A new approach to detect a target with an unknown amplitude in clutter is proposed. The received signal models under two hypotheses, H/sub 0/and H/sub 1/, are assumed to be the same, except that the target amplitude is zero under H/sub 0/. Using the Bayesian approach, it is shown that the likelihood ratio can be calculated as the ratio of the prior to posterior probabilities of the target amplitude. Based on this relation, a new method for target detection in Gaussian clutter is presented. This method is applied to cases with both known and unknown clutter statistics and in each case, white and coloured clutters are considered. Simulation results show that the proposed detector has a much better performance compared with conventional generalised likelihood ratio test detectors.     

Detection probabilities for photon-counting avalanche photodiodes applied to a laser radar system

Arrays of photon-counting avalanche photodiodes with time-resolved readout can improve the performance of three-dimensional laser radars. A comparison of the detection and false-alarm probabilities for detectors in linear mode and in Geiger mode is shown. With low background radiation their performance is comparable. It is shown that in both cases it will be necessary to process several laser shots of the same scene to improve detection and reduce the false-alarm rate. Additional calculations show that the linear mode detector is much better at detecting targets behind semitransparent obscurations such as vegetation and camouflage nets.     

Multiple-input multiple-output over-thehorizon radar: experimental results

Results from an experiment that applied one class of multiple-input multiple-output (MIMO) waveform techniques to over-the-horizon radar (OTHR) are reported. The experiment objective was to demonstrate that adaptive transmitter beamforming could be used in an appropriately design radar to reject spatially discrete Doppler-spread clutter. In the particular MIMO radar architecture that the authors call non-causal transmit beamforming, conventional or adaptive transmitter beamforming occurs following waveform transmission, propagation, scatter from targets and clutter sources, return propagation and finally signal reception. In the case reported herein spatially discrete clutter was successfully rejected to the noise floor of the radar return with rejection in excess of 35 dB achieved using common adaptive algorithms and straightforward training data selection. As part of the rejection algorithm the transmitted waveform direction-of-departure (DOD) from the transmitter array to the target was estimated and used as the preserved steer direction in the adaptive beamformer. The DOD estimates agree well with the geometrically determined true values. The demonstration of non-causal transmit beamforming suggests that it will be possible to create multiple simultaneous adaptive range-dependent transmitter beams with an appropriately designed OTHR. This has several applications including for the mitigation of Doppler-spread clutter.     

Non-coherent radar CFAR detection based on goodness-of-fit tests

This paper considers the problem of constant false alarm rate (CFAR) detection of radar targets using multiple observations. In the Gaussian clutter scenario, the structure of the optimum (uniformly most powerful) CFAR detector is rather simple, but when the clutter is heavy-tailed, that is non-Gaussian distributed, the derivation of the optimal detector becomes infeasible. For this latter relevant case, a new CFAR algorithm is porposed based on goodness-of-fit (GoF) tests. The performance of the proposed detector is numerically investigated through Monte Carlo simulations assuming heavy-tailed Weibull and Lognormal distributed clutter. Numerical results shown that, in heavy-tailed clutter and also when several interfering targets exist, the proposed detector outperforms the conventional CFAR detector based on binary integration. Performance is also tested processing real sea clutter data collected by a non-coherent navigation radar     

Outlier suppression in adaptive filtering through de-emphasis weighting

A de-emphasis weighting approach is used to suppress the effect of outliers in background samples during the formation of a sample covariance matrix. The approach is relevant to a broad range of adaptive filtering techniques. Results from processing simulated and real coherent radar data using de-emphasis weighting are compared with results using no outlier suppression and censored sample matrix inversion pruning methods. De-emphasis techniques are shown to produce the most robust detection performance when outliers are present and are also shown to have minimal performance impact when clutter is homogeneous, that is no outliers present     

Non-adaptive multiple-input, multiple-output radar techniques for reducing clutter

Multiple-input, multiple-output (MIMO) radars enhance performance by transmitting and receiving coded waveforms from multiple locations. This paper describes MIMO techniques that can be used to improve radar performance, especially in airborne Ground Moving Target Indicator (GMTI) applications. The authors begin by showing how MIMO techniques can lower airborne radar clutter to noise ratios (CNRs). This results in smaller losses when observing stationary or low-velocity targets. Next, the authors consider the implementation of MIMO radar modes using electronically scanned arrays (ESAs). Specifically, the authors show how MIMO techniques, applied to subarray-based ESAs, can cause high grating lobes and/or reduced search rates. To address this problem, the authors describe new space ? time waveform coding techniques that can be used to improve performance. Two space ? time waveform encoding approaches are proposed: (i) an overlapped virtual transmit subarray approach, and (ii) a beamspace MIMO approach. A third approach, involving conventional MIMO waveforms and irregular subarrays, is also briefly considered.     

Network Intrusion Detection Using CFAR Abrupt-Change Detectors

In this paper, the constant false alarm rate (CFAR) detectors are proposed for network intrusion detection. By using an autoregressive system to model the network traffic, predictor error is shown to closely follow a Gaussian distribution. CFAR detector approaches are then developed on the prediction error distribution. In the present study, we consider the optimal CFAR, the cell-averaging CFAR, and the order statistics CFAR. The use of these CFAR techniques can significantly improve the detection performance. In addition, we propose the use of fusion of these CFAR detectors by using Dempster-Shafer and Bayesian techniques. Computer simulations based on the DARPA traffic data show that the proposed approach achieves higher detection probabilities than the conventional detection method. Even under different types of attacks, the intrusion detection performances based on the proposed CFAR detectors shows consistent improvement.     

Constant false-alarm rate algorithm based on test cell information

A new constant false-alarm rate (CFAR) detection algorithm, designated as switching CFAR (S-CFAR), is proposed and analysed. The S-CFAR algorithm selects CFAR reference samples using the magnitude of the sample in the cell under test, which is an information that has not been exploited in any other existing CFAR detectors. S-CFAR closed-form analysis is presented, and comparisons with other representative CFAR algorithms are given. An S-CFAR detector can be tuned such that it has a small CFAR loss when operating on a homogeneous background while achieving improved robustness in the presence of interfering targets and clutter power transition. The S-CFAR detector is also simple to design and implement since no sample ordering is required.     

Models and performance evaluation for multiple-input multiple-output space-time adaptive processing radar

Signal and clutter modelling and optimum performance evaluation for multiple-input multiple-output (MIMO)-based space?time adaptive processing radar is addressed. A signal model is developed to account for both code diverse MIMO (c-MIMO) and frequency diverse MIMO (f-MIMO), and a general framework on performance evaluation is presented to take into account various waveform configurations including phased array (PA), partially correlated MIMO and ideally orthogonal MIMO. The proposed framework evaluates the system performance through optimum processing (OP) gain and transmit array (TA) gain. The OP gain is in turn evaluated by the number of available space?time measurements (ASMs) that depends on the number of clutter degrees of freedom (clutter NDoF) relative to the system degrees of freedom (system NDoF). The waveform diversity introduced by MIMO, especially f-MIMO, could significantly enhance the OP gain by increasing the number of ASMs. Hence, in OP-gain-limited scenarios, where the overall performance significantly degrades despite the TA gains, the preferable configuration in terms of optimum performance would be ordered as f-MIMO, c-MIMO, and last, PA, that is, no MIMO.     

Knowledge-aided covariance matrix estimation: a MAXDET approach

The authors consider the problem of knowledge-aided covariance matrix estimation and its application to adaptive radar detection. The authors assume that an a priori (knowledge-based) estimate of the disturbance covariance M, derived from a physical scattering model of the terrain and/or of the environment, is available. Hence, starting from a set of secondary data, the authors evaluate the maximum likelihood (ML) estimate of M assuming that it lies in a suitable neighbourhood of the a priori estimate and formulate this ML estimation in terms of a convex optimisation problem which falls within the class of MAXDET problems. Both the cases of unstructured and structured disturbance covariance are considered. At the analysis stage, the authors assess the performance of the new knowledge-aided covariance estimators both in terms of estimation error and detection probability achievable by a class of adaptive detectors. The results highlight that, if the a priori knowledge is reliable, satisfactory levels of performance can be achieved with considerably less training data than those exploited by conventional algorithms.     

Co-channel interference suppression by time and range adaptive processing in bistatic high-frequency surface wave synthesis impulse and aperture radar

Intense co-channel interference (CCI) severely depresses the target detection in high-frequency surface wave radar (HFSWR). In this study, the CCI cancellation algorithm by time and range adaptive processing is proposed for a novel HFSWR ? bistatic HF surface wave synthesis impulse and aperture radar. With the real data, the interference is firstly modelled and then its features are investigated. The analyses show that the same interference prevails over a few but different bins through different channels, whereas the echoes are relatively weak and exist in all bins; in range domain, however, the interference takes over all the bins including positive and negative bins and will spread over the same and considerable Doppler area through different channels, whereas the echoes appear only in partially positive bins. On the basis of the features, the interference covariance matrix can be obtained by selecting the samples whose average power is much higher than that of the others in time domain and in range domain; the samples from either or both of beyond the detectable bins and negative bins can be selected for training. The interference can be cancelled by projecting the polluted data into the orthogonal subspace, constructing the projecting matrix with the eigenvectors associated with large eigenvalues of the covariance matrix. Finally, the segment handling and samples requirement are also discussed for reducing the computation burden. The experimental results are provided to demonstrate the performance of the method.     

认知型水声通信时空自适应处理算法研究

针对浅海水声通信面临的复杂干扰环境,提出了一种适用于水声通信系统的认知型时空自适应处理算法。该算法在传统时空自适应阵列处理算法的基础上引入了干扰认知的功能,通过干扰认知处理降低计算量,并且实现干扰样本的选取,确保自适应算法的可靠收敛。利用了协方差矩阵特征分解和多重信号分类(Multiple Signal Classification, MUSIC)估计干扰的个数,辅助选取盲源分离算法中的维度和时空自适应算法中的空域维度,进行算法降维处理。针对采用盲源分离后的各干扰,采用包络检测法提取时域特征,采用短时傅里叶变换(Short-time Fourier Transform, STFT)方法提取时频谱特征,分析干扰特征,实现干扰分类,从而实现干扰样本的选取。通过计算机仿真验证了该认知处理算法的性能。     

Improving adaptive receivers performance in molecular communication via diffusion

Forthcoming applications for molecular communications (MC) such as drug‐delivery and health monitoring will require robust receiver capabilities to mitigate channel memory and inter symbol interference caused by previous transmitted symbols. Here, the authors introduce an adaptive weighted algorithm to reduce the influence of these factors. This novel signal detection is deployed on to a concentration‐based MC system with absorbing receiver which is based on the so‐called first passage time concept. The proposed detector has low complexity and does not require explicit channel knowledge. To evaluate authors’ proposed algorithm, a theoretical approach is developed to derive the bit error rate (BER). Numerical results also carried out to verify the accuracy of these formulations and establish that the new detector will achieve better performance in comparison with other common low‐complex detectors under certain scenarios. Additionally, the authors propose a simple pre‐coding technique to combat the sequence of consecutive ones in low ISI scenarios. Also a comparison between detectors is given, which is based on the variation of distance, symbol period, signal‐to‐noise ratio (SNR), and number of molecules.Inspec keywords: radio receivers, error statistics, signal detection, intersymbol interferenceOther keywords: low‐complex detectors, low ISI scenarios, symbol period, signal‐to‐noise ratio, adaptive receivers performance, molecular communication, drug‐delivery, health monitoring, robust receiver capabilities, channel memory, inter symbol interference, previous transmitted symbols, adaptive weighted algorithm, novel signal detection, concentration‐based MC system, absorbing receiver, passage time concept, low complexity, explicit channel knowledge, authors, theoretical approach, bit error rate, numerical results     

Blind adaptive and iterative interference cancellation receiver structures based on the constant modulus criterion in multipath channels

Blind adaptive and iterative interference cancellation (IC) receiver structures for direct sequence code division multiple access systems in multipath channels are proposed. A code-constrained constant modulus design criterion based on constrained optimisation techniques and adaptive algorithms for receiver and channel parameter estimation are described for successive IC (SIC) and parallel IC (PIC) detectors and a new hybrid IC (HIC) scheme in scenarios subject to multipath fading. The proposed HIC structure combines the strengths of linear, SIC and PIC receivers and is shown to outperform the conventional linear, SIC and PIC structures. A novel iterative detection approach that generates different cancellation orders and selects the most likely symbol estimate on the basis of the instantaneous minimum constant modulus criterion is also proposed and combined with the new HIC structure to further enhance performance. Simulation results for an uplink scenario assess the algorithms, the proposed blind adaptive IC detectors against existing receivers and evaluate the effects of error propagation of the new cancellation techniques.     

基于二阶锥规划的稳健低旁瓣自适应波束形成

针对水下成像时圆弧阵常规波束旁瓣级较高,当存在强干扰时容易带来较多虚警的缺点,提出一种基于二阶锥规划的稳健低旁瓣自适应波束形成方法。该方法通过对波束旁瓣进行优化设计,可以将波束旁瓣级进行严格控制,并进一步结合协方差矩阵重构法,使波束形成器的稳健性得到提高,最后将该波束优化问题转化为二阶锥规划问题进行求解。计算机仿真结果表明,相较于其他算法来说,文中算法在波束旁瓣级得到严格控制的同时,可以在存在各类失配的情况下获得更高的输出信干噪比,稳健性更高。水池实验进一步验证了该方法的有效性,该研究成果可以在声呐成像领域应用。     

双基雷达空时自适应处理中的杂波特性

基于合适的双基坐标系，得出双基雷达的等距离曲线方程是一族椭圆．文章研究了对齐、平行和垂直三种不同的双基几何配置时的方位-多普勒杂波轨迹特性，并将之与单基情形进行对比，从而揭示了双基杂波的非静态特性；以杂波功率谱特性为工具研究了杂波特性对空时自适应处理性能的影响，双基杂波的非静态特性使得空时自适应处理器的杂波频谱变宽，最小可检测目标速度变大，处理性能大大恶化．本文的分析方法适用于机载和星载双基雷达．     

Performance analysis of multiple-input multipleoutput singular value decomposition transceivers during fading and other cell interference

A generalised method is derived to compute the error probabilities of singular value decomposition (SVD)-based receivers for a multiple-input multiple-output (MIMO) system with uncoded transmission. The method can be used for a wide class of flat fading environments, including independent and identically distributed (i.i.d.) and semi-correlated Rayleigh and i.i.d. Ricean channels. Although the method is applied to equal-power binary phase shift keying, it can easily be extended to higher-order M-ary phase shift keying (M-PSK) and M-ary quadrature amplitude modulation (M-QAM) signal constellations and adaptive 'water-filling' schemes. The error probability curves derived from closed-form formulas and simulations demonstrate very close agreement. The error performances of channel inversion, minimum mean square error and zero forcing receivers are compared with the SVD receiver for a single-user system. The impact of multiple users is considered by studying the performance of an adaptive MIMO SVD transmission scheme operating in a cellular environment. In particular, the effect of inter-cell interference on the performance of the scheme is quantified, modelling the interference as increased Gaussian noise. A number of cellular layouts are examined and the impact of the resulting singal-to-interference and noise ratio on the constellation sizes that can be supported, the BER and so on is considered. The primary metric used for our performance analysis is the error-free transmission rate, which is derived for our adaptive system. For the cellular scenarios considered, it can be found that the effect of interference is considerable and the performance of the adaptive MIMO SVD scheme is only marginally better than that provided by conventional diversity methods.     

Hybrid detectors for wireless orthogonal frequency division multiplexing with code division multiplexing systems based on reliability information

Orthogonal frequency division multiplexing with code division multiplexing (OFDM-CDM) is attractive for the next generation high-speed wireless systems due to the fact that the performance of OFDM-CDM systems can be considerably improved by employing a joint detection scheme such as the maximum likelihood (ML) detector. However, the complexity of the ML detector increases rapidly as the number of orthogonal spreading codes and/or the number of bits per modulation symbol increase. In this study, the authors introduce a unified detection model and propose two hybrid detectors, which combine zero forcing (ZF) with successive interference cancellation (SIC) and sphere detection (SD) algorithms, respectively. After obtaining the initial solution from the front-end ZF receiver, the proposed back-end algorithms are adopted to extend the potential solution list and search for the final result. The objective is to utilise the combination of a simplified linear equaliser and a comprehensive detection scheme to achieve enhanced performance and offer alternatives to the more complex and channel-estimation-sensitive minimum mean squared error (MMSE) scheme. The results show that the proposed hybrid detectors are able to achieve superior performance compared to the MMSE scheme and provides a significant performance improvement compared to the conventional OFDM counterpart.